Sponsor
Krishnamoorthy and Deutsch are thankful for the support provided by the NSF Grant EF 0531870 for working on the research presented in this paper.
Published In
Algorithms for Molecular Biology
Document Type
Article
Publication Date
10-7-2010
Subjects
Mutagenicity testing, Mutagenesis -- Forecasting, Mutagenesis -- Mathematical models
Abstract
Background: Mutagenesis is commonly used to engineer proteins with desirable properties not present in the wild type (WT) protein, such as increased or decreased stability, reactivity, or solubility. Experimentalists often have to choose a small subset of mutations from a large number of candidates to obtain the desired change, and computational techniques are invaluable to make the choices. While several such methods have been proposed to predict stability and reactivity mutagenesis, solubility has not received much attention. Results: We use concepts from computational geometry to define a three body scoring function that predicts the change in protein solubility due to mutations. The scoring function captures both sequence and structure information. By exploring the literature, we have assembled a substantial database of 137 single- and multiple-point solubility mutations. Our database is the largest such collection with structural information known so far. We optimize the scoring function using linear programming (LP) methods to derive its weights based on training. Starting with default values of 1, we find weights in the range [0,2] so that predictions of increase or decrease in solubility are optimized. We compare the LP method to the standard machine learning techniques of support vector machines (SVM) and the Lasso. Using statistics for leave-one-out (LOO), 10-fold, and 3-fold cross validations (CV) for training and prediction, we demonstrate that the LP method performs the best overall. For the LOOCV, the LP method has an overall accuracy of 81%.
DOI
10.1186/1748-7188-5-33
Persistent Identifier
http://archives.pdx.edu/ds/psu/7154
Citation Details
Tian, Y., Deutsch, C., & Krishnamoorthy, B. (2010). Scoring Function to Predict Solubility Mutagenesis. Algorithms for Molecular Biology, 5:33.
Description
© 2010 Tian et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Originally published in Algorithms for Molecular Biology (http://www.almob.org/) and can be found at (http://www.almob.org/content/5/1/33).